1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks
Akash Das, Satya Ranjan Nayak, B. K. Singh
In this study, we employ a conventional deep neural network (NN) framework integrated with physics-based constraints to predict charged hadron multiplicity () in hea…
Study of Particle Production in Au+Au Collisions at 11.5-200 GeV Using HYDJET++ Framework
Gauri Devi, Satya Ranjan Nayak, B. K. Singh
Using the HYDJET++ model, we study the production of multi-strange particles( and ) in Au+Au collisions at 11.5, 19.6, 27, 39, and 200 GeV as functions of t…
Effects of Geometric configuration in relativistic isobaric collisions at GeV
Akash Das, Satya Ranjan Nayak, B. K. Singh
In this work, we present a study on the effects of nuclear deformation (,) and surface diffuseness () on the charged hadron multiplicity () and ellipt…
Two-particle cumulant distribution: a simulation study of higher moments
Satya Ranjan Nayak, Akash Das, B. K. Singh
In this work, we have shown the two-particle correlations of charged hadrons in d-Au collisions at 200 GeV in PYTHIA8/Angantyr simulations. These correlations were studied at diffe…
Separating non-collective effects in d-Au collisions
Satya Ranjan Nayak, Akash Das, B. K. Singh
In this work, we present the multiplicity and yield of charged hadrons and particle ratios in d-Au collisions at GeV using the PYTHIA8/Angantyr. The model repr…